title: Classification of stand-to-sit and sit-to-stand movement from low frequency EEG with locality preserving dimensionality reduction.
abstract: Recent studies have demonstrated decoding of lower extremity limb kinematics from noninvasive electroencephalography (EEG), showing feasibility for development of an EEG-based brain-machine interface (BMI) to restore mobility following paralysis. Here, we present a new technique that preserves the statistical richness of EEG data to classify movement state from time-embedded low frequency EEG signals. We tested this new classifier, using cross-validation procedures, during sit-to-stand and stand-to-sit activity in 10 subjects and found decoding accuracy of greater than 95% on average. These results suggest that this classification technique could be used in a BMI system that, when combined with a robotic exoskeleton, can restore functional movement to individuals with paralysis.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Bulea
authors.forename: Saurabh
authors.initials: S
authors.lastname: Prasad
authors.forename: Atilla
authors.initials: A
authors.lastname: Kilicarslan
authors.forename: Jose L
authors.initials: JL
authors.lastname: Contreras-Vidal
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D062207:Brain-Computer Interfaces; D016002:Discriminant Analysis; D004569:Electroencephalography; D004576:Electromyography; D006801:Humans; D009043:Motor Activity / Q000502:physiology*; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted